For the algorithm in [1]} , the network structure includes one feature embedding layer, three graph convolution layers, and three fully-connected layers.
More details could be found in the original literature.
For the 3DGCN in [2]} , we prefer the max pooling for comparison.
Similarly, the details of the 3DGCN could be found in the original literature.
The proposed algorithm employs three convolution layers and three fully connected layers, similar to the CGCNN.
The following parameters were shared for all tasks.
The batch size was set as 16, and a weight decay was \(0.01\) .
The above setting was helpful to enhance the generalization of the prediction model. Besides, we used Adam as the optimizer, and the initial learning rate was set as \(0.001\)  for all tasks.
The experiments were performed on a virtual machine with 24 vCPU (2.5GHz), 112 G ram, and 1 V100 GPU (5120 CUDA Cores and 32Gb ram).
